Documents

Over the years, the JRC has produced many publications. These are found in this section. They have been sub-divided in various categories (see Subcategory buttons below). All more than 550 documents can also be inspected irrespective of the category (see 'All documents' below).

Publications in Journals include more than 390 published papers from the Soil Group in the JRC (EU Soil Observatory). Most of the papers refer to the last 10 years (2013-2023). In many cases the papers document the datasets published in ESDAC.

As example statistics, Since the establishement of the EUSO,  the group published:

  • 23 papers in 2020,
  • 27 papers in 2021
  • 40 papers in 2022
  • 46 papers in 2023

Most of them in high impact journals including Nature Communicaitons, Climate Change, Global Change Biology, etc. Almost all the publications are Open Access. As publications, we present articles published in peer-review journals indexed in Scopus or Web of Science.

 

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Soil health in the Western Balkans
Soil health in the Western Balkans
Resource Type: Documents, Scientific-Technical Reports, Maps & Documents
Year: 2022
Attachments: PDF icon KJNA31163ENN.en_.pdf
This study is a compilation of evidence to support the development of a soil component for a JRC Science for Policy Report on the “Status of Environment and Climate in the Western Balkans”1. This document attempts to benchmark a range of issues affecting soil health with considerations on the accession progress for an eventual Soil Health Law under the 2030 Soil Strategy.
The outcomes reported here are based on a literature review of 139 sources, bilateral exchanges with national soil experts in all Western Balkans countries, and on the personal experience of the authors. It should be emphasised that current data are scarce, and as such, the results should be considered as a primarily assessment and not definitive.
Based on the results of this study it is concluded that soil degradation is prevalent and extensive throughout the Western Balkans region. Soils are under pressure, but the intensity of various soil health indicators varies between them and among the countries. Climate change was not part of this study. Nevertheless, its impacts will be relevant in the coming decades, if not preventive mitigation, remediation, and adaptation actions will be needed to lessen their impacts.
Download the Report.
 
LANDSUPPORT Decision Support Systems: performance at EU, country, regional and local scale
LANDSUPPORT Decision Support Systems: performance at EU, country, regional and local scale
Resource Type: Maps & Documents, Documents, Scientific-Technical Reports
Year: 2022

Developing a methodology for the analysis of the anthropogenic intervention on natural capital is an ambitious process. The LANDSUPPORT project implemented a Decision Support System (DSS) to help support better land use planning. The LANDSUPPORT DSS aimed at creating an innovative and intuitive environment that will improve the readability of measured and modelled geo-information, making it accessible to a wide range of stakeholders. A cross-evaluation analysis has been developed to measure the ability to support policy-related stakeholders and help them make informed decisions.

In the field of land use and land management, appropriate data are essential to support effective land planning in which different sources of available information need to be synthesised to provide an holistic picture of their combined effect. Policy stakeholders play a pivotal role in designing the DSS to be transversal and able to cope with issues at local, national and regional scales. National and regional land monitoring projects are often evenly disseminated or unsuitable for policy needs. Many attempts were recently made to overcome this problem, and the concept of decision dashboards is now extensively used to address these challenges. DSS are growing in popularity. They are often distributed as open access online interfaces that use georeferenced data and visualization tools to present model metrics and key performance indicators. Data behind DSSs can be static or dynamic as it is for the LANDSUPPORT DSS. The set of tools developed at different scales within different domains of application, are regularly updated and obtained from the state-of-the-art scientific development. With the user in mind, the LANDSUPPORT DSS encourages an extensive range of policy stakeholders to perform land use analysis, improving the tool's usability and making informed decisions for land sustainability. Decisions on land management at local scale are already benefiting from such information systems.

For the EU, specific tools were developed, tailored to the specific needs of stakeholders. For instance, in line with its strategic plan, Italy uses specific functionality and indicator sets to support its decision-making across agriculture sectors (viticulture and oliviculture tools are tailored examples of the DSS dynamism).
Another goal of DSS is to improve geodata integration across highly compartmentalized sectors. Each tool which appears in the dashboard will undergo regular improvements as the backend platform is refined. The LANDSUPPORT tool allows for the analysis of soil sealing in the land take tool, the land degradation neutrality tool (SDG 15.3.1 indicator), and the climate change resilience through another dedicated tool embedding future scenarios of climate change based on CORDEX ensemble models and (Representative Concentration Pathways, RCPs). Given the challenge of identifying and gaining access to good quality data from local partners, the LANDSUPPORT project fostered the capacity building among partners through workshops and other interactions and drew attention to better data management and improved accessibility and sharing. Embedding data access and its use in end-user organizations is vital to generating and sustaining interest in the dashboards.

This report presents the analysis carried out to test the LANDSUPPORT DSS’s ability to support EU policies when applied at the EU scale. Key EU policies of interest include the 7th Environmental Action Porgamme; COM 2006/231 Soil Strategy, Dir 2000/60/EC Water Directive; Dir 2007/2/EC INSPIRE Directive. and the land-related targets of the 2030 Sustainable Development Goals (SDGs) in particular, SDGs 2 “Zero Hunger”, 3 “god healh and well-being”, 13 “Climate action”, 15, with a special emphasis to the key SDG 15.3.1, “achieving a land degradation-neutral world” (LDN) and climate change (CC) mitigation goals.

Developing a high-resolution land use/land cover map by upgrading CORINE’s agricultural components using detailed national and pan-European datasets
Developing a high-resolution land use/land cover map by upgrading CORINE’s agricultural components using detailed national and pan-European datasets
Resource Type: Maps & Documents, Documents, Publications in Journals
Year: 2022

The agricultural uses of the Coordination of Information on the Environment Land Cover (CLC) dataset suffer from limitations such as temporal stationarity, low spatial resolution, broad and rather simplified grouping of classes. The study attempts to address these shortcomings, using as test site the Sperchios River catchment, Central Greece. The Greek ‘branch’ of the Land Parcel Identification System, Beneficiaries’ Declarations (BD) and CLC inventories were utilized to develop hybrid layers, deriving from their harmonization, sequential incorporation and progressive update (BD → BD-ilot → BD-ilot-CLC). The final layer constitutes the new object-oriented Land Use/Land Cover map. Remote sensing data (Sentinel-2) was used to validate the accuracy of the BD, subject to the most frequent errors. The new map retains the key advantages of CLC yet is now characterized by highly detailed spatial resolution and the explicit description of the different cultivated farmlands included.

10.1080/10106049.2022.2041107

48-year effect on organic carbon and nitrogen stocks in two soil types in northwestern Tunisia
48-year effect on organic carbon and nitrogen stocks in two soil types in northwestern Tunisia
Resource Type: Documents, Publications in Journals, Maps & Documents
Year: 2022

Information on spatial and temporal changes in the soil organic carbon (SOC) and soil total nitrogen (STN) stocks are crucial to support sustainable land management strategies. However, such information is scarce mainly for arid and semi-arid regions. The present research aimed to determine SOC and STN stock dynamics with depths during the 1971–2019 period. Thus, two soil types were selected in north Tunisia: a Luvisol under forest vegetation and a Cambisol under cereal crop and analyzed for SOC, STN, rock fraction, and bulk density from the surface (0–30 cm), middle (30–50 cm), and deep layers (50–100 cm) in 1971, 2005, 2012, and 2019. SOC and STN contents decreased with depth in both soils. The Luvisol exhibited the highest SOC and STN contents. From 1971 to 2019, SOC and STN contents decreased more in the surface layer in both soils with sharpest decrease for STN than SOC. The stocks were calculated taking into account the rock fractions avoiding overestimation. Stocks recorded in the surface layer corresponded to 68% of total SOC stock and 58% of total STN stock in both soils. From 1971 to 2019, the reduction of SOC and STN stocks was more important in the Cambisol than in the Luvisol in all layers, mainly in the surface layer. Such reductions could have important implications for soil fertility and global warming

10.1007/s12517-022-09860-3

Computation of total soil organic carbon stock and its standard deviation from layered soils
Computation of total soil organic carbon stock and its standard deviation from layered soils
Resource Type: Documents, Publications in Journals, Maps & Documents
Year: 2022

To assess carbon sequestration in the agricultural and natural systems, it is usually required to report soil organic carbon (SOC) as mass per unit area (Mg ha-1) for a single soil layer (e.g., the 0-0.3 m ploughing layer). However, if the SOC data are reported as relative concentration (g kg-1 or %), it is required to compute the SOC stock and its standard deviation (SD) for a given layer as the product of SOC concentration and bulk density (BD). For a proper computation, it is required to consider that these two variables are correlated. Moreover, if the data are already reported as SOC stock for multiple sub-layers (e.g., 0-0.15 m, 0.15-0.3 m) it is necessary to compute the SOC stock and its SD for a single soil layer (e.g., 0-0.3 m). The correlation between stocks values from adjacent and non-adjacent soil sub-layers must be accounted to compute the SD of the single soil layer.

The present work illustrates the methodology to compute SOC stock and its SD for a single soil layer based on SOC concentration and BD also from multiple sub-layers. An Excel workbook automatically computes the means of stocks and SD saving the results in a ready-to-use database.
•Computation of a carbon (SOC) stock and its standard deviation (SD) from the product between SOC concentration and bulk density (BD), being correlated variables.
•Computation of a SOC stock and its SD from the sum of SOC stocks of multiple correlated sub-layers.
•An Excel workbook automatically computes the means of SOC stocks and SD and saves the results in a ready-to-use database.

10.1016/j.mex.2022.101662

Improving the phosphorus budget of European agricultural soils
Improving the phosphorus budget of European agricultural soils
Resource Type: Documents, Publications in Journals, Maps & Documents
Year: 2022

Despite phosphorus (P) being crucial for plant nutrition and thus food security, excessive P fertilization harms soil and aquatic ecosystems. Accordingly, the European Green Deal and derived strategies aim to reduce P losses and fertilizer consumption in agricultural soils. The objective of this study is to calculate a soil P budget, allowing the quantification of the P surpluses/deficits in the European Union (EU) and the UK, considering the major inputs (inorganic fertilizers, manure, atmospheric deposition, and chemical weathering) and outputs (crop production, plant residues removal, losses by erosion) for the period 2011–2019.

The Land Use/Cover Area frame Survey (LUCAS) topsoil data include measured values for almost 22,000 samples for both available and total P. With advanced machine learning models, we developed maps for both attributes at 500 m resolution. We estimated the available P for crops at a mean value of 83 kg ha−1 with a clear distinction between North and South. The ratio of available P to the total P is about 1:17.

The inorganic fertilizers and manure contribute almost equally as P inputs (mean 16 ± 2 kg P ha−1 yr−1 at 90 % confidence level) to agricultural soils, with high regional variations depending on farming practices, livestock density, and cropping systems. The P outputs came mainly from the exportation by the harvest of crop products and residues (97.5 %) and, secondly, by erosion. Using a sediment distribution model, we quantified the P fluxes to river basins and sea outlets.

In the EU and UK, we estimated an average surplus of 0.8 kg P ha−1 yr−1 with high variability between countries with some regional variations. The P annual budget at regional scale showed ample possibility to improve P management by both reducing inputs in regions with high surplus (and P soil available) and rebalancing fertilization in those at risk of soil fertility depletion.

10.1016/j.scitotenv.2022.158706

 

Soil priorities in the European Union
Soil priorities in the European Union
Resource Type: Documents, Publications in Journals, Maps & Documents
Year: 2022

Soils provide crucial ecosystem services such as the provision of food, carbon sequestration and water purification. Soil is the largest terrestrial pool of carbon, hosts more than 25% of all biodiversity and provides 95–99% of food to 8 billion people. The European Union (EU) puts the concept of healthy soils at the core of the European Green Deal to achieve climate neutrality, zero pollution, sustainable food provision and a resilient environment.

Given the European Union's objective to become the first climate neutral continent by 2050, the European Commission adopted a series of communications for a greener Europe. In 2020, an ambitious package of measures were presented within the Biodiversity 2030, Farm to Fork and Chemicals Strategies, as well as the Circular Economy Action Plan and the European Climate Law, which included actions to protect soils (Montanarella and Panagos, 2021). In 2021, these were followed by the Fit for 55 package, the Zero Pollution Action Plan and the EU Soil Strategy for 2030. All these policies include provisions relevant to soils to achieve the ambitious objectives of the EU Green Deal.

10.1016/j.geodrs.2022.e00510

European Soil Data Centre 2.0: Soil data and knowledge in support of the EU policies
European Soil Data Centre 2.0: Soil data and knowledge in support of the EU policies
Resource Type: Documents, Publications in Journals, Maps & Documents
Year: 2022

The European Soil Data Centre (ESDAC), hosted by the European Commission's Joint Research Centre (JRC), is the focal point for soil data, support to policy making and awareness raising for the European Union (EU). Established in 2006 to provide harmonised soil-related information for the EU Member States, and ESDAC currently hosts 88 datasets, 6000 maps, six atlases, 500 scientific publications, and a copious amount of soil-related material. Through its data repository publishing activity, ESDAC has licensed over 50,000 datasets during the past 15 years; 8500 of them in 2021 alone. It has published 140 monthly newsletters and is followed by more than 12,000 subscribed users, which receive regular updates. This article addresses the use, usability, and usefulness of ESDAC. About 75% of the ESDAC users come from academia and the research community while the remaining 25% includes public administration (at EU, national, regional, and local level) and the private sector. In addition, we provide some insights of the datasets evaluation and how they have been developed. The general ESDAC vision is to provide evidence underlying EU soil-relevant policies and to facilitate the access to relevant data for research. ESDAC is an integral part of the recently established European Union Soil Observatory (EUSO), with a target to have an even stronger role in supporting EU and regional policies.

10.1111/ejss.13315

Phosphorus plant removal from European agricultural land
Phosphorus plant removal from European agricultural land
Resource Type: Documents, Publications in Journals, Maps & Documents
Year: 2022

Phosphorus (P) is an important nutrient for all plant growth and it has become a critical and often imbalanced element in modern agriculture. A proper crop fertilization is crucial for production, farmer profits, and also for ensuring sustainable agriculture. The European Commission has published the Farm to Fork (F2F) Strategy in May 2020, in which the reduction of the use of fertilizers by at least 20% is among one of the main objectives. Therefore, it is important to look for the optimal use of P in order to reduce its pollution effects but also ensure future agricultural production and food security. It is essential to estimate the P budget with the best available data at the highest possible spatial resolution. In this study, we focused on estimating the P removal from soils by crop harvest and removal of crop residues. Specifically, we attempted to estimate the P removal by taking into account the production area and productivity rates of 37 crops for 220 regions in the European Union (EU) and the UK. To estimate the P removal by crops, we included the P concentrations in plant tissues (%), the crop humidity rates, the crop residues production, and the removal rates of the crop residues. The total P removal was about 2.55 million tonnes (Mt) (± 0.23 Mt), with crop harvesting having the larger contribution (ca. 94%) compared to the crop residues removal. A Monte-Carlo analysis estimated a ± 9% uncertainty. In addition, we performed a projection of P removal from agricultural fields in 2030. By providing this picture, we aim to improve the current P balances in the EU and explore the feasibility of F2F objectives.

10.1007/s00003-022-01363-3

Global rainfall erosivity projections for 2050 and 2070
Global rainfall erosivity projections for 2050 and 2070
Resource Type: Documents, Publications in Journals, Maps & Documents
Year: 2022

The erosive force of rainfall (rainfall erosivity) is a major driver of soil, nutrient losses worldwide and an important input for soil erosion assessments models. Here, we present a comprehensive set of future erosivity projections at a 30 arc-second (∼1 km2) spatial scale using 19 downscaled General Circulation Models (GCMs) simulating three Representative Concentration Pathways (RCPs) for the periods 2041–2060 and 2061–2080. The future rainfall erosivity projections were obtained based on a Gaussian Process Regression (GPR) approach relating rainfall depth to rainfall erosivity through a series of (bio)climatic covariates. Compared to the 2010 Global Rainfall erosivity baseline, we estimate a potential average increase in global rainfall erosivity between 26.2 and 28.8% for 2050 and 27–34.3% for 2070. Therefore, climate change and the consequential increase in rainfall erosivity is the main driver of the projected + 30–66% increase in soil erosion rates by 2070.

Our results were successfully compared with 20 regional studies addressing the rainfall erosivity projections. We release the whole dataset of future rainfall erosivity projections composed of 102 simulation scenarios, with the aim to support further research activities on soil erosion, soil conservation and climate change communities. We expect these datasets to address the needs of both the Earth system modeling community and policy makers. In addition, we introduce a modeling approach to estimate future erosivity and make further assessments at global and continental scales.

10.1016/j.jhydrol.2022.127865

In defence of soil biodiversity: Towards an inclusive protection in the European Union
In defence of soil biodiversity: Towards an inclusive protection in the European Union
Resource Type: Documents, Publications in Journals, Maps & Documents
Year: 2022

Since soil biodiversity sustains above-ground life, the European Union (EU) has recently announced its new Soil Strategy to better protect soil ecosystems as part of the Biodiversity Strategy for 2030. Also, the EU's Farm to Fork Strategy and the Zero Pollution Action Plan aim for soil protection. However, the status of soil biodiversity protection has not been comprehensively assessed. Therefore, we explored regulatory, incentive-based and knowledge-based instruments and strategic policy documents at the EU and national levels to determine whether they adequately protect soil biodiversity. Our review of 507 literature references concluded that only eight EU member states explicitly address threats to soil biodiversity in 14 regulatory instruments while 13 countries mainly focus on implicit threats to soil biodiversity, whereas six countries do not consider soil biodiversity. At the EU level, current directives and regulations only tackle individual threats to soil biodiversity. An EU-wide, legally binding protection could ensure a standardised minimum level of soil biodiversity protection while preventing surging costs of not acting. The EU Soil Health Law foreseen for 2023 could couple land management practices beneficial for soil biodiversity with incentive-based instruments. Simultaneously, models should be designed to predict soil biodiversity, considering soil biodiversity's spatial and temporal heterogeneity.

10.1016/j.biocon.2022.109475

LUCAS Soil Biodiversity and LUCAS Soil Pesticides, new tools for research and policy development
LUCAS Soil Biodiversity and LUCAS Soil Pesticides, new tools for research and policy development
Resource Type: Documents, Publications in Journals, Maps & Documents
Year: 2022

The European Green Deal puts a healthy environment at the core of policy-making initiatives in the European Union (EU). Soil, due to its nature, is a central actor to be considered when developing and implementing actions in many areas, from biodiversity and agriculture to climate and pollution. New means for monitoring the impact of decisions taken in these sectors are needed. The Land Use and Coverage Area frame Survey (LUCAS modules of Soil Biodiversity and Pesticides provide both policy-makers and the research community with tools in this direction. The first component corresponds to the largest analysis of soil biodiversity across the EU through molecular biology techniques. The second component is the most extensive harmonised pan-European assessment of pesticide residues in agricultural soils. Specific features, together with policy and research potential of these instruments for achieving Green Deals targets under the Biodiversity 2030 Strategy, the Farm to Fork Strategy and the Zero Pollution Action Plan, are presented. All generated data are made available to the public through the EU Soil Observatory and European Soil Data Centre.

10.1111/ejss.13299

Challenges in the Geo-Processing of Big Soil Spatial Data
Challenges in the Geo-Processing of Big Soil Spatial Data
Resource Type: Documents, Publications in Journals, Maps & Documents
Year: 2022

This study addressed a critical resource—soil—through the prism of processing big data at the continental scale. Rapid progress in technology and remote sensing has majorly improved data processing on extensive spatial and temporal scales. Here, the manuscript presents the results of a systematic effort to geo-process and analyze soil-relevant data. In addition, the main highlights include the difficulties associated with using data infrastructures, managing big geospatial data, decentralizing operations through remote access, mass processing, and automating the data-processing workflow using advanced programming languages. Challenges to this study included the reproducibility of the results, their presentation in a communicative way, and the harmonization of complex heterogeneous data in space and time based on high standards of accuracy. Accuracy was especially important as the results needed to be identical at all spatial scales (from point counts to aggregated countrywide data). The geospatial modeling of soil requires analysis at multiple spatial scales, from the pixel level, through multiple territorial units (national or regional), and river catchments, to the global scale. Advanced mapping methods (e.g., zonal statistics, map algebra, choropleth maps, and proportional symbols) were used to convey comprehensive and substantial information that would be of use to policymakers. More specifically, a variety of cartographic practices were employed, including vector and raster visualization and hexagon grid maps at the global or European scale and in several cartographic projections. The information was rendered in both grid format and as aggregated statistics per polygon (zonal statistics), combined with diagrams and an advanced graphical interface. The uncertainty was estimated and the results were validated in order to present the outputs in the most robust way. The study was also interdisciplinary in nature, requiring large-scale datasets to be integrated from different scientific domains, such as soil science, geography, hydrology, chemistry, climate change, and agriculture.

10.3390/land11122287

Global assessment of storm disaster-prone areas
Global assessment of storm disaster-prone areas
Resource Type: Documents, Publications in Journals, Maps & Documents
Year: 2022

Advances in climate change research contribute to improved forecasts of hydrological extremes with potentially severe impacts on human societies and natural landscapes. Rainfall erosivity density (RED), i.e. rainfall erosivity (MJ mm hm-2 h-1 yr-1) per rainfall unit (mm), is a measure of rainstorm aggressiveness and a proxy indicator of damaging hydrological events. Here, using downscaled RED data from 3,625 raingauges worldwide and log-normal ordinary kriging with probability mapping, we identify damaging hydrological hazard-prone areas that exceed warning and alert thresholds (1.5 and 3.0 MJ hm-2 h-1, respectively). Applying exceedance probabilities in a geographical information system shows that, under current climate conditions, hazard-prone areas exceeding a 50% probability cover ~31% and ~19% of the world’s land at warning and alert states, respectively. RED is identified as a key driver behind the spatial growth of environmental disruption worldwide (with tropical Latin America, South Africa, India and the Indian Archipelago most affected).

10.1371/journal.pone.0272161
 

GloSEM: High-resolution global estimates of present and future soil displacement in croplands by water erosion
GloSEM: High-resolution global estimates of present and future soil displacement in croplands by water erosion
Resource Type: Documents, Publications in Journals, Maps & Documents
Year: 2022

Healthy soil is the foundation underpinning global agriculture and food security. Soil erosion is currently the most serious threat to soil health, leading to yield decline, ecosystem degradation and economic impacts. Here, we provide high-resolution (ca. 100 × 100 m) global estimates of soil displacement by water erosion obtained using the Revised-Universal-Soil-Loss-Equation-based Global Soil Erosion Modelling (GloSEM) platform under present (2019) and future (2070) climate scenarios (i.e. Shared Socioeconomic Pathway [SSP]1–Representative Concentration Pathway [RCP]2.6, SSP2–RCP4.5 and SSP5–RCP8.5). GloSEM is the first global modelling platform to take into account regional farming systems, the mitigation effects of conservation agriculture (CA), and climate change projections. We provide a set of data, maps and descriptive statistics to support researchers and decision-makers in exploring the extent and geography of soil erosion, identifying probable hotspots, and exploring (with stakeholders) appropriate actions for mitigating impacts. In this regard, we have also provided an Excel spreadsheet that can provide useful insights into the potential mitigating effects of present and future alternative CA scenarios at the country level.

10.1038/s41597-022-01489-x

Global analysis of cover management and support practice factors that control soil erosion and conservation
Global analysis of cover management and support practice factors that control soil erosion and conservation
Resource Type: Documents, Publications in Journals, Maps & Documents
Year: 2022

Cover management and support practices largely control the magnitude and variability of soil erosion. Although soil erosion models account for their importance (particularly by C- and P-factors in the Revised Universal Soil Loss Equation), obtaining spatially explicit quantitative field data on these factors remains challenging. Hence, also our insight into the effects of soil conservation measures at larger spatial scales remains limited. We analyzed the variation in C- and P-factors caused by human activities and climatic variables by reviewing 255 published articles reporting measured or calculated C- and P-factor values. We found a wide variation in both factor values across climatic zones, land use or cover types, and support practices. The average C-factor values decreased from arid (0.26) to humid (0.15) climates, whereas the average P-factor values increased (from 0.33 to 0.47, respectively). Thus, support practices reduce soil loss more effectively in drylands and drought-prone areas. The global average C-factor varies by one order of magnitude from cropland (0.34) to forest (0.03). Among the major crops, the average C-factor was highest for maize (0.42) followed by potato (0.40), among the major orchard crops, it was highest for olive (0.31), followed by vineyards (0.26). The P-factor ranged from 0.62 for contouring in cropland plots to 0.19 for trenches in uncultivated land. The C-factor results indicate that cultivated lands requiring intensive site preparation and weeding are most vulnerable to soil loss by sheet and rill erosion. The low P-factor for trenches, reduced tillage cultivation, and terraces suggests that significantly decreased soil loss is possible by implementing more efficient management practices. These results improve our understanding of the variation in C- and P-factors and support large-scale integrated catchment management interventions by applying soil erosion models where it is difficult to empirically determine the impact of particular land use or cover types and support practices: the datasets compiled in this study can support further modeling and land management attempts in different countries and geographic regions.

10.1016/j.iswcr.2021.12.002

Simulating event-scale rainfall erosivity across European climatic regions
Simulating event-scale rainfall erosivity across European climatic regions
Resource Type: Documents, Publications in Journals, Maps & Documents
Year: 2022

Soil erosion is time compressed into a number of episodic erosive rainfall events with an associated potential to detach and transport soil particles (rainfall erosivity), each possessing unique spatial and temporal characteristics. Rainfall erosivity events in Europe follow extreme value distributions in which a limited number of rainstorms dominate the long-term budget of available erosive energy. To combat soil erosion in Europe in a targeted manor, timely erosion mitigation measures should derive from dynamic model simulations that incorporate spatially and temporally distributed estimations of rainfall erosivity. Rain gauge measurements from singular points are typically used to quantify rainfall erosivity, however the spatial uniqueness of rainfall presents a key limitation to dynamically model rainfall across broad spatial scales with a limited number of point measurements. Discretised gridded precipitation datasets with a widespread (e.g. continental) spatial coverage potentially offer an opportunity to adequately replicate the dynamics of rainfall erosivity events, however their performance remains poorly tested in the pan-European context.

This study builds upon the comprehensive Rainfall Erosivity Database at European Scale (REDES) archive of over 300,000 events from 1181 gauge stations to develop a two-step modelling process: 1) firstly, optimal monthly models were fitted and evaluated between gauge-recorded rainfall depth and rainfall erosivity (EI30) across European climatic regions to develop a European-scale parameter surface, 2) secondly, three datasets (EMO-5 (6-hr), E-OBS (24-hr), UERRA MESCAN-SURFEX (24hr)) were directly evaluated via a grid-to-point analysis based on their ability to simulate the station-specific event rainfall erosivity timeseries at a random selection of 32 locations. EMO-5 (Nash-Sutcliffe model efficiency mean = 0.24) outperformed other tested gridded datasets, showing the capability to adequately replicate the event number, timing, and their average magnitude. A higher model performance in Northern compared with Southern European climatic regions, in which characteristically higher and spatially-complex event rainfall erosivity magnitudes are found, was symptomatic of a poor ability of grid-based simulations to replicate the magnitudes of events in the outer extents of the frequency-magnitude spectrum. The absence of a clear global systematic predictive bias amongst simulated locations suggests the need for future upscaling of this analysis to the entire European REDES dataset to fully understand and correct for the method-derived bias in a climate region-specific way.

10.1016/j.catena.2022.106157

 A new high resolution object-oriented approach to define the spatiotemporal dynamics of the cover-management factor in soil erosion modelling
A new high resolution object-oriented approach to define the spatiotemporal dynamics of the cover-management factor in soil erosion modelling
Resource Type: Documents, Publications in Journals, Maps & Documents
Year: 2022

The cover management factor (C-factor) calculation requires the assessment of the intra‐annual spatiotemporal variability of biomass cover, owed to the natural growth cycle of vegetation and the impact of agriculture on land cover. However, this is frequently omitted, and the vegetation conditions are approximated by assigning constant values to static classified Land Use/Land Cover (LULC) maps, such as the Coordination of Information on the Environment (CORINE) Land Cover (CLC). Using as test site the Sperchios River catchment, Central Greece, this study introduces a new approach to estimate C-factor in a spatiotemporally exhaustive manner. The goal is to increase estimation accuracy in erosion modelling applications. The C-factor computations are performed on monthly scale, based on LULC maps that portray the basin’s agricultural areas in unprecedented detail. The methodology involves the use of a biophysical index, namely Fraction of Vegetation Cover (Fcover) and empirical literature data on crop types. Fcover was developed from Sentinel-2 (S2) imagery in 10-m analysis. Such analysis (compared to the 300-m one provided by the EU) is a major improvement towards a more precise estimation of C-factor. The study identified the monthly C-factor fluctuation at basin scale, and the most susceptible months seasons at localities in terms of land cover/soil loss potential. The higher C-factor values were acquired in October and the lower in May. Mean annual (numerical) C-factor complies with the value of July. All monthly values are significantly higher – almost double – than the mean annual stationary one. The revealed patterns would not have been detected in a lower temporal (e.g., annual) resolution without the incorporation of vegetation density seasonality. The study shows high reproducibility and upscaling potential, as the utilized datasets are available in all European Union (EU) Member States, having similar structure, thus they can be harmonized towards a unified continental approach.

10.1016/j.catena.2022.106149

Matrix representation of lateral soil movements: scaling and calibrating CE-DYNAM (v2) at a continental level
Matrix representation of lateral soil movements: scaling and calibrating CE-DYNAM (v2) at a continental level
Resource Type: Documents, Publications in Journals, Maps & Documents
Year: 2022

Promoting sustainable soil management is a possible option for achieving net-zero greenhouse gas emissions in the future. Several efforts in this area exist, and the application of spatially explicit models to anticipate the effect of possible actions on soils at a regional scale is widespread. Currently, models can simulate the impacts of changes on land cover, land management, and the climate on the soil carbon stocks. However, existing modeling tools do not incorporate the lateral transport and deposition of soil material, carbon, and nutrients caused by soil erosion. The absence of these fluxes may lead to an oversimplified representation of the processes, which hinders, for example, a further understanding of how erosion has been affecting the soil carbon pools and nutrients through time. The sediment transport during deposition and the sediment loss to rivers create dependence among the simulation units, forming a cumulative effect through the territory. If, on the one hand, such a characteristic implies that calculations must be made for large geographic areas corresponding to hydrological units, on the other hand, it also can make models computationally expensive, given that erosion and redeposition processes must be modeled at high resolution and over long timescales. In this sense, the present work has a three-fold objective. First, we provide the development details to represent in matrix form a spatially explicit process-based model coupling sediment, carbon, and erosion, transport, and deposition (ETD) processes of soil material in hillslopes and valley bottoms (i.e., the CE-DYNAM model). Second, we illustrate how the model can be calibrated and validated for Europe, where high-resolution datasets of the factors affecting erosion are available. Third, we presented the results for a depositional site, which is highly affected by incoming lateral fluxes from upstream lands. Our results showed that the benefits brought by the matrix approach to CE-DYNAM enabled the before-precluded possibility of applying it on a continental scale. The calibration and validation procedures indicated (i) a close match between the erosion rates calculated and previous works in the literature at local and national scales, (ii) the physical consistency of the parameters obtained from the data, and (iii) the capacity of the model in predicting sediment discharge to rivers in locations observed and unobserved during its calibration (model efficiency (ME) =0.603, R2=0.666; and ME =0.152, R2=0.438, respectively). The prediction of the carbon dynamics on a depositional site illustrated the model's ability to simulate the nonlinear impact of ETD fluxes on the carbon cycle. We expect that our work advances ETD models' description and facilitates their reproduction and incorporation in land surface models such as ORCHIDEE. We also hope that the patterns obtained in this work can guide future ETD models at a European scale.

10.5194/gmd-15-7835-2022

Occurrence and erosion susceptibility of German Pelosols and international equivalents
Occurrence and erosion susceptibility of German Pelosols and international equivalents
Resource Type: Documents, Publications in Journals, Maps & Documents
Year: 2022
Pelosols are the Soil of the Year 2022 in Germany, Austria and Switzerland. They represent soils with a high clay content (≥45%) in the diagnostic P horizon. Pelosols are nutrient-rich, have a strong capacity for swelling and shrinking, have a challenging water balance with a high portion of nonplant available water and are affected by high traction. Such special characteristics make them challenging soils under agricultural management. The occurrence, land use management and soil erosion risk of Pelosols in Germany were investigated and compared to their clay-rich soil counterparts on a global scale. A high percentage (63%) of Pelosols in Germany are under agricultural use, from which two-thirds are arable farming. Simultaneously, Pelosols have a high risk for soil erosion by water and are the fourth most endangered soil type compared to all soil types in Germany. The average soil erosion loss of Pelosols used for agricultural practices assessed by the Revised Universal Soil Loss Equation (RUSLE) is 2.24 t ha−1 year−1 compared to an average erosion loss of all agriculturally used soils in Germany of 1.65 t ha−1 year−1. From an international perspective, Pelosols in Germany are mostly mapped as haplic Cambisols or haplic Luvisols, as they do not necessarily meet the diagnostic properties of the clay-rich Vertisol soil type. Most Vertisols are classified as Pelosols, but Pelosols do not necessarily fulfil the diagnostic criteria of Vertisols. Vertisols on a global scale have an even higher soil erosion risk than Pelosols in Germany (3.5 t ha−1 year−1).
 
Exploring the possible role of satellite-based rainfall data in estimating inter-and intra-annual global rainfall erosivity
Exploring the possible role of satellite-based rainfall data in estimating inter-and intra-annual global rainfall erosivity
Resource Type: Documents, Publications in Journals, Maps & Documents
Year: 2022

Despite recent developments in modeling global soil erosion by water, to date, no substantial progress has been made towards more dynamic inter- and intra-annual assessments. In this regard, the main challenge is still represented by the limited availability of high temporal resolution rainfall data needed to estimate rainfall erosivity. As the availability of high temporal resolution rainfall data will most likely not increase in future decades since the monitoring networks have been declining since the 1980s, the suitability of alternative approaches to estimate global rainfall erosivity using satellite-based rainfall data was explored in this study. For this purpose, we used the high spatial and temporal resolution global precipitation estimates obtained with the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) Climate Prediction Center MORPHing (CMORPH) technique. Such high spatial and temporal (30 min) resolution data have not yet been used for the estimation of rainfall erosivity on a global scale. Alternatively, the erosivity density (ED) concept was also used to estimate global rainfall erosivity. The obtained global estimates of rainfall erosivity were validated against the pluviograph data included in the Global Rainfall Erosivity Database (GloREDa). Overall, results indicated that the CMORPH estimates have a marked tendency to underestimate rainfall erosivity when compared to the GloREDa estimates. The most substantial underestimations were observed in areas with the highest rainfall erosivity values. At the continental level, the best agreement between annual CMORPH and interpolated GloREDa rainfall erosivity maps was observed in Europe, while the worst agreement was detected in Africa and South America. Further analyses conducted at the monthly scale for Europe revealed seasonal misalignments, with the occurrence of underestimation of the CMORPH estimates in the summer period and overestimation in the winter period compared to GloREDa. The best agreement between the two approaches to estimate rainfall erosivity was found for fall, especially in central and eastern Europe. Conducted analysis suggested that satellite-based approaches for estimation of rainfall erosivity appear to be more suitable for low-erosivity regions, while in high-erosivity regions (> 1000–2000 MJ mm ha−1 h−1 yr−1) and seasons (> 150–250 MJ mm ha−1 h−1 month−1), the agreement with estimates obtained from pluviographs (GloREDa) is lower. Concerning the ED estimates, this second approach to estimate rainfall erosivity yielded better agreement with GloREDa estimates compared to CMORPH, which could be regarded as an expected result since this approach indirectly uses the GloREDa data. The application of a simple-linear function correction of the CMORPH data was applied to provide a better fit to GloREDa and correct systematic underestimation. This correction improved the performance of CMORPH, but in areas with the highest rainfall erosivity rates, the underestimation was still observed. A preliminary trend analysis of the CMORPH rainfall erosivity estimates was also performed for the 1998–2019 period to investigate possible changes in the rainfall erosivity at a global scale, which has not yet been conducted using high-frequency data such as CMORPH. According to this trend analysis, an increasing and statistically significant trend was more frequently observed than a decreasing trend.

10.5194/hess-26-1907-2022

Estimation of rainfall erosivity factor in Italy and Switzerland using Bayesian optimization based machine learning models
Estimation of rainfall erosivity factor in Italy and Switzerland using Bayesian optimization based machine learning models
Resource Type: Documents, Publications in Journals, Maps & Documents
Year: 2022
This study aimed to evaluate the estimation accuracy of rainfall erosivity (R-factor) in Italy and Switzerland through five Machine learning (ML) models (Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Gradient Boosting (GB), and eXtreme Gradient Boost (XGB)) tuned with optimal hyperparameters. To build the ML model, high temporal resolution (HTR) rainfall data were collected from 297 rain-gauge stations located in the study area. To estimate the RUSLE-based R-factor through the models, the rainfall amount for each rainfall event, the rainfall duration, and the maximum 60-min intensity were used as input data. The datasets for training/validation and testing consisted of rainfall data from 287 and 10 stations, respectively. In a second phase, each ML model was trained through 10-fold cross validation based on training and validation data. For hyperparameter adjustment, the models were optimized using the Bayesian optimization algorithm (BOA). The R-factor estimation performance of each ML model through cross validation improved from 6.1% to 62.8% as hyperparameters were optimized through BOA. In particular, ensemble models such as RF, GB, and XGB were superior to other models with an accuracy performance of 0.9 or even more. And the RF showed an excellent estimation performance (R 2 = 0.965, NSE = 0.958, RMSE = 44.993 MJ mm ha−1h−1, and MAE = 13.901 MJ mm ha−1h−1) for test stations, followed by GB and XGB with similar performance. However, the R-factor for the extremely intense rainfall event estimated by the ML models showed a significant difference from the RUSLE-based R-factor. This result implies that although the ML model built in this study can reasonably estimate the R-factor in the general rainfall event, additional training and validation through securing various rainfall event data is required to improve estimation accuracy on an extreme rainfall event.
 

10.1016/j.catena.2021.105957

Monitoring gully erosion in the European Union: A novel approach based on the Land Use/Cover Area frame survey (LUCAS)
Monitoring gully erosion in the European Union: A novel approach based on the Land Use/Cover Area frame survey (LUCAS)
Resource Type: Documents, Publications in Journals, Maps & Documents
Year: 2022

The European Commission's Thematic Strategy for Soil Protection (COM(2012)46) identified soil erosion as an important threat to European Union's (EU) soil resources. Gully erosion is an important but hitherto poorly understood component of this threat. Here we present the results of an unprecedented attempt to monitor the occurrence of gully erosion across the EU and UK. We integrate a soil erosion module into the 2018 LUCAS Topsoil Survey, which was conducted to monitor the soil health status across the EU and to support actions to prevent soil degradation. We discuss and explore opportunities to further improve this method. The 2018 LUCAS Topsoil Survey consisted of soil sampling (0–20 cm depth) and erosion observations conducted in ca. 10% (n = 24,759) of the 238,077 Land Use/Cover Area frame Survey (LUCAS) 2018 in-field survey sites. Gully erosion channels were detected for ca. 1% (211 sites) of the visited LUCAS Topsoil sites. Commission (false positives, 2.5%) and omission errors (false negatives, 5.6%) were found to be low and at a level that could not compromise the representativeness of the gully erosion survey. Overall, the findings indicate that the tested 2018 LUCAS Topsoil in-field gully erosion monitoring system is effective for detecting the incidence of gully erosion. The morphogenesis of the mapped gullies suggests that the approach is an effective tool to map permanent gullies, whereas it appears less effective to detect short-lived forms like ephemeral gullies. Spatial patterns emerging from the LUCAS Topsoil field observations provide new insights on typical gully formation sites across the EU and UK. This can help to design further targeted research activities. An extension of this approach to all LUCAS sites of 2022 would significantly enhance our understanding of the geographical distribution of gully erosion processes across the EU. Repeated every three years, LUCAS soil erosion surveys would contribute to assess the state of gully erosion in the EU over time. It will also enable monitoring and eventually predicting the dynamics of gully erosion. Data collected were part of the publicly available Gully Erosion LUCAS visual assessment (GE-LUCAS v1.0) inventory.

10.1016/j.iswcr.2021.09.002

Agricultural Adaptation to Climate Change
Agricultural Adaptation to Climate Change
Resource Type: Maps & Documents, Documents, Publications in Journals
Year: 2022

Climate change is associated with elevated temperatures, more intense rainfalls and longer and hotter droughts. These changes add stress to soil and water resources, which form the foundation for a productive resilient agriculture. Crop management practices often stress soil and water resources leading to loss of soil organic carbon, increased soil erosion and degraded water quality. However, selected management systems can improve soil and water quality or limit their degradation, even in light of anticipated climate change stressors. This chapter identifies approaches to increase the adoption of recognized favorable practices in different countries or regions. Three primary approaches seem to exist: (1) incentive-based with no or minor regulatory component; (2) regulatory-dominated with government exercising authority over producer practice options; and (3) long-term planning addressing spatial and temporal land management elements and adoption of those plans with a combination of government support and regulatory authority.

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Soil Erosion in Europe: From Policy Developments to Models, Indicators and New Research Challenges
Soil Erosion in Europe: From Policy Developments to Models, Indicators and New Research Challenges
Resource Type: Documents, Publications in Journals, Maps & Documents
Year: 2022

We present the developments on soil erosion modelling at European scale to respond to the policy needs. The Joint Research Centre (JRC) of the European Commission has developed the European Soil Erosion Modelling Platform (EUSEMP) to support the agro-environmental policies in the European Union (EU). The major component of EUSEMP is an hybrid soil erosion model named RUSLE2015 (RUSLE-based) to estimate soil loss by water erosion. The model runs with updated input layers (years: 2000, 2010 and 2016) and provides baselines for evaluating the current status of agricultural soils in the EU, evaluating the impact of agri-environmental policies on land management and making projections of soil loss by water erosion in 2050. In addition, EUSEMP includes also the Revised Wind Erosion Equation (RWEQ) to access wind erosion in EU arable lands and another component to access soil loss due to harvesting crops. Another component of EUSEMP is the sediment delivery model WaTEM/SEDEM to estimate net soil loss. Finally, the erosion models are coupled with the biogeochemical CENTURY model to estimate soil organic carbon losses by water erosion. EUSEMP targets to integrate the sediments transfer datasets with soil pollution data (e.g. mercury, copper) and nutrient losses (e.g. phoshorus).

DOI: 10.1007/978-981-16-7916-2_21